TY - GEN
T1 - Validating clusters using the Hopkins statistic
AU - Banerjee, Amit
AU - Davé, Rajesh N.
PY - 2004
Y1 - 2004
N2 - A novel scheme for cluster validity using a test for random position hypothesis is proposed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by a partitioning algorithm. A test statistic such as the well-known Hopkins statistic could be used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here using two artificially constructed test data sets.
AB - A novel scheme for cluster validity using a test for random position hypothesis is proposed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by a partitioning algorithm. A test statistic such as the well-known Hopkins statistic could be used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here using two artificially constructed test data sets.
UR - http://www.scopus.com/inward/record.url?scp=11144327629&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=11144327629&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2004.1375706
DO - 10.1109/FUZZY.2004.1375706
M3 - Conference contribution
AN - SCOPUS:11144327629
SN - 0780383532
T3 - IEEE International Conference on Fuzzy Systems
SP - 149
EP - 153
BT - 2004 IEEE International Conference on Fuzzy Systems - Proceedings
T2 - 2004 IEEE International Conference on Fuzzy Systems - Proceedings
Y2 - 25 July 2004 through 29 July 2004
ER -